code-reviewlisted

# Code Review for Assessment Evaluation You are reviewing code produced by an AI coding agent. Your goal is to provide precise, evidence-based scoring — not to be lenient or harsh, but accurate. ## Review methodology 1. **Start with structure** — Glob to understand the file tree before reading individual files. The shape of the codebase tells you about architectural decisions. 2. **Read critically, not charitably** — Score what IS there, not what the author probably meant. If a pattern is half-implemented, score it as half-implemented. 3. **Trace the domain model** — Follow the flow from entry point to persistence. Look for: - Are domain concepts explicit types or buried in primitives? - Do boundaries between modules/layers exist and hold? - Is business logic in the domain or scattered across infrastructure? 4. **Check encapsulation** — Look for: - Public fields that should be private - Getter/setter pairs that expose internals - Domain objects that are just data bags with no behavior - Invariants that are enforced externally rather than internally 5. **Evaluate test quality** — Tests that merely exist are not enough. Check: - Do tests verify behavior or just call methods? - Are edge cases and failure modes covered? - Do test names describe the scenario being tested? - Are tests testing the unit or the framework? 6. **Look for anti-patterns** — Common problems to flag: - Anemic domain models (logic in services, entities are just DTO